如何提取由用于对象检测的 Tensorflow 模型生成的图像中检测到的对象、对象类别、对象 ID 的输出分数？
我想将所有这些详细信息存储到单独的变量中，以便以后将它们存储在数据库中。
使用与此链接中找到的相同的代码https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
请帮我解决这个问题。
我试过了
打印（str（output_dict ['检测类'] [0]），“：”，str（output_dict ['检测_分数'] [0]））
这有效并给出了概率最高的类的对象 id 和分数。但我也想提取类名以及图像中存在的所有对象的分数、ID 和名称
输出示例：图像中有两只狗。当我打印结果时，我得到概率最高的对象的 ID 和分数 [在本例中为 94%] 我也想打印对象名称以及图像中所有其他对象的类似详细信息 https://i.stack.imgur.com/33x5U.png
您可能需要一些有关张量流对象检测的知识背景，这里简短而快速的解决方案可能就是您所期望的方式：
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
image_np = load_image_into_numpy_array(image)
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
objects = []
threshold = 0.5 # in order to get higher percentages you need to lower this number; usually at 0.01 you get 100% predicted objects
for index, value in enumerate(classes[0]):
object_dict = {}
if scores[0, index] > threshold:
object_dict[(category_index.get(value)).get('name').encode('utf8')] = \
scores[0, index]
objects.append(object_dict)
print (objects)
print(len(np.where(scores[0] > threshold)[0])/num_detections[0])
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np)
希望这有帮助。
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